• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1749-1760
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• 2022

In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.393-407
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• 2012

This study focuses on the correction of topographic effects caused by a combination of solar elevation and azimuth, and topographic relief in single optical remote sensing imagery, and by a combination of changes in position of the sun and topographic relief in comparative analysis of multi-temporal imageries. For the Jeju Island, Republic of Korea, where Mt. Halla and various cinder cones are located, a Landsat 7 ETM+ imagery and ASTER GDEM data were used to normalize the topographic effects on the imagery, using two topographic normalization methods: cosine correction assuming a Lambertian condition and assuming a non-Lambertian c-correction, with kernel sizes of $3{\times}3$, $5{\times}5$, $7{\times}7$, and $9{\times}9$ pixels. The effects of each correction method and kernel size were then evaluated. The c-correction with a kernel size of $7{\times}7$ produced the best result in the case of a land area with various land-cover types. For a land-cover type of forest extracted from an unsupervised classification result using the ISODATA method, the c-correction with a kernel size of $9{\times}9$ produced the best result, and this topographic normalization for a single land cover type yielded better compensation for topographic effects than in the case of an area with various land-cover types. In applying the relative radiometric normalization to topographically normalized three multi-temporal imageries, more invariant spectral reflectance was obtained for infrared bands and the spectral reflectance patterns were preserved in visible bands, compared with un-normalized imageries. The results show that c-correction considering the remaining reflectance energy from adjacent topography or imperfect atmospheric correction yielded superior normalization results than cosine correction. The normalization results were also improved by increasing the kernel size to compensate for vertical and horizontal errors, and for displacement between satellite imagery and ASTER GDEM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.503-509
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• 2013

The KOMPSAT-3 (Korea Multi-Purpose Satellite-3), was launched on May 18, 2012, is an optical high-resolution observation mission of the Korea Aerospace Research Institute and provides RPC(Rational Polynomial Coefficient) for ground coordinate determination. It is however need to adjust because RPC absorbs effects of interior-exterior orientation errors. In this study, to obtain the suitable adjustment parameters of the vendor-provided RPC of the KOMPSAT-3 images, six types of adjustment models were implemented. As results, the errors of two and six adjustment parameters differed approximately 0.1m. We thus propose the two parameters model, the number of control points are required the least, to adjust the KOMPSAT-3 R PC. According to the increasing the number of control points, RPC adjustment was performed. The proposed model with a control point particularly did not exceed a maximum error 3m. As demonstrated in this paper, the two parameters model can be applied in RPC adjustment of KOMPSAT-3 stereo image.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.79-90
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• 2017

Urban environment represent one of the most dynamic regions on earth. As in other countries, forests, green areas, agricultural lands are rapidly changing into residential or industrial areas in South Korea. Monitoring such rapid changes in land use requires rapid data acquisition, and satellite imagery can be an effective method to this demand. In general, SAR(Synthetic Aperture Radar) satellites acquire images with an active system, so the brightness of the image is determined by the surface roughness. Therefore, the water areas appears dark due to low reflection intensity, In the residential area where the artificial structures are distributed, the brightness value is higher than other areas due to the strong reflection intensity. If we use these characteristics of SAR images, settlement areas can be extracted efficiently. In this study, extraction of settlement areas was performed using TerraSAR-X of German high-resolution X-band SAR satellite and KOMPSAT-5 of South Korea, and object-oriented image classification method using the image segmentation technique is applied for extraction. In addition, to improve the accuracy of image segmentation, the speckle divergence was first calculated to adjust the reflection intensity of settlement areas. In order to evaluate the accuracy of the two satellite images, settlement areas are classified by applying a pixel-based K-means image classification method. As a result, in the case of TerraSAR-X, the accuracy of the object-oriented image classification technique was 88.5%, that of the pixel-based image classification was 75.9%, and that of KOMPSAT-5 was 87.3% and 74.4%, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.113-126
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• 2021

This study was conducted to analyze the forest status and deforestation area changes of the DMZ region in North Korea based on satellite images. Using growing and non-growing season's RapidEye satellite images, land cover of the North Korean DMZ was classified into stocking land(conifer, deciduous, mixed), deforested land(unstocked mountain, cultivated mountain, bare mountain), and non-forest areas. Deforestation rates in the Yeonan-baecheon, Beopdong-Pyeonggang, Heoyang-Geumgang and Tongcheon-Goseong district were calculated as 14.24%, 16.75%, 5.98%, and 16.63% respectively. Forest fire and land use change of forest were considered as the main causes of deforestation of DMZ. Changes in deforestation area were analyzed through Google Earth images. As a results, it was shown that the area of deforestation was on a decreasing trend. This study can be used as basic data for establishing inter-Korean border region's forest cooperation strategies by providing forest spatial information on the North Korea's DMZ.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Atmosphere
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• v.19 no.1
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• pp.37-51
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• 2009

Kinematic characteristics of a heavy rainfall event occurred in Changma front are analyzed using synoptic weather charts, satellite imagery and NCEP(National Centers for Environmental Prediction) / NCAR(National Centers for Atmospheric Research) reanalysis data. The heavy rainfall is accompanied with mesoscale rain clouds developing over the Southwest region of Korea during the period from 0300 LST to 2100 LST 25 June 2006. The surface cyclone in the Changma front is generated and developed rapidly when it meets following vertical conditions: The maximum value of relative vorticity is appeared at 700 hPa and is extended gradually near the surface. It is thought that the vertical structure of relative vorticity is closely related with the descent of strong wind zone exceeding $10ms^{-1}$. The jet core at 200 hPa is shifted southward and extended downward and the low-level jet stream associated with upper-level jet stream appeared at 850 hPa. Kinematic features of heavy rainfall system at cyclone-generating point are as follows: In the generating stage of cyclone, the relative vorticity below 850 hPa increased and the convergence below 850 hPa and the divergence at 400 hPa are intensified by southward movement of jet core at 200 hPa. The heavy rainfall system seems to locate to the south of the exit region of upper-level jet streak; In the developing stage of cyclone, the relative vorticity below 850 hPa and the convergence near surface are further strengthened and upward vertical velocity between 850 hPa and 200 hPa is increased.

• Journal of the Korean GEO-environmental Society
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• v.9 no.6
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• pp.43-51
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• 2008

This study is to evaluate the NPP (Net Primary Production) distribution in the Geum River basin from NOAA/AVHRR satellite imagery data. It is supposed that the natural vegetation condition and the NPP has the linear relationship. The NPP from natural vegetation increases proportional to the annual net radiation (Rn), where radiative dryness index (RDI) is a proportional constant connecting net radiation to NPP. Normalized Difference Vegetation Index (NDVI) is used for monitoring vegetation change, and iNDVI (integrated NDVI) for annual analysis. The iNDVI has a close relation to Rn and NPP, which can be used effectively for estimating NPP distribution of where the meteorological data is unavailable. The purpose of this study is to propose a simple method to get NPP in the Geum river basin.